BACKGROUND: As the worldwide prevalence of chronic illness increases so too does the demand for novel treatments to improve chronic illness care. Quantifying improvement in chronic illness care from the patient perspective relies on the use of validated patient-reported outcome measures. In this analysis we examine the psychometric and scaling properties of the Patient Assessment of Chronic Illness Care (PACIC) questionnaire for use in the United Kingdom by applying scale data to the non-parametric Mokken double monotonicity model. METHODS: Data from 1849 patients with long-term conditions in the UK who completed the 20-item PACIC were analysed using Mokken analysis. A three-stage analysis examined the questionnaire's scalability, monotonicity and item ordering. An automated item selection procedure was used to assess the factor structure of the scale. Analysis was conducted in an 'evaluation' dataset (n = 956) and results were confirmed using an independent 'validation' (n = 890) dataset. RESULTS: Automated item selection procedures suggested that the 20 items represented a single underlying trait representing "patient assessment of chronic illness care": this contrasts with the multiple domains originally proposed. Six items violated invariant item ordering and were removed. The final 13-item scale had no further issues in either the evaluation or validation samples, including excellent scalability (Ho = .50) and reliability (Rho = .88). CONCLUSIONS: Following some modification, the 13-items of the PACIC were successfully fitted to the non-parametric Mokken model. These items have psychometrically robust and produce a single ordinal summary score. This score will be useful for clinicians or researchers to assess the quality of chronic illness care from the patient's perspective.